Journal of Biomolecular NMR

, Volume 40, Issue 4, pp 263–276 | Cite as

Structure-based protein NMR assignments using native structural ensembles

  • Mehmet Serkan Apaydın
  • Vincent Conitzer
  • Bruce Randall DonaldEmail author


An important step in NMR protein structure determination is the assignment of resonances and NOEs to corresponding nuclei. Structure-based assignment (SBA) uses a model structure (“template”) for the target protein to expedite this process. Nuclear vector replacement (NVR) is an SBA framework that combines multiple sources of NMR data (chemical shifts, RDCs, sparse NOEs, amide exchange rates, TOCSY) and has high accuracy when the template is close to the target protein’s structure (less than 2 Å backbone RMSD). However, a close template may not always be available. We extend the circle of convergence of NVR for distant templates by using an ensemble of structures. This ensemble corresponds to the low-frequency perturbations of the given template and is obtained using normal mode analysis (NMA). Our algorithm assigns resonances and sparse NOEs using each of the structures in the ensemble separately, and aggregates the results using a voting scheme based on maximum bipartite matching. Experimental results on human ubiquitin, using four distant template structures show an increase in the assignment accuracy. Our algorithm also improves the robustness of NVR with respect to structural noise. We provide a confidence measure for each assignment using the percentage of the structures that agree on that assignment. We use this measure to assign a subset of the peaks with even higher accuracy. We further validate our algorithm on data for two additional proteins with NVR. We then show the general applicability of our approach by applying our NMA ensemble-based voting scheme to another SBA tool, MARS. For three test proteins with corresponding templates, including the 370-residue maltose binding protein, we increase the number of reliable assignments made by MARS. Finally, we show that our voting scheme is sound and optimal, by proving that it is a maximum likelihood estimator of the correct assignments.


Automated NMR assignments Normal mode analysis NMR structural biology Protein flexibility via structural ensembles Structural bioinformatics 



Backbone root mean square distance


Bipartite graph


Chemical shift


N-terminal domain of enzyme I




G-α interacting protein


Homology detection


Maximum bipartite matching


Maltose-binding protein


Maximum likelihood estimator


Molecular replacement


Normal mode analysis


Nuclear magnetic resonance


Nuclear overhauser effect


Nuclear vector replacement




Residual dipolar coupling


Structure-based assignment


Streptococcal protein G



We thank Drs. C. Bailey-Kellogg, P. Zhou, Mr. D. Keedy, Mr. J. MacMaster, Mr. C. Tripathy, Mr. A. Yan, Mr. M. Zeng and all members of the Donald Lab for discussions and comments. This work is supported by a grant to B.R.D. from the National Institute of Health (R01 GM-65982).


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Copyright information

© US Government 2008

Authors and Affiliations

  • Mehmet Serkan Apaydın
    • 1
  • Vincent Conitzer
    • 1
  • Bruce Randall Donald
    • 1
    • 2
    Email author
  1. 1.Department of Computer ScienceDuke UniversityDurhamUSA
  2. 2.Department of BiochemistryDuke University Medical CenterDurhamUSA

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